Chronic kidney disease (CKD) is a complex condition marked by a gradual decline in kidney function, which can ultimately progress to end-stage renal disease (ESRD). Globally, the prevalence of the CKD ranges from 8% to 16%, with about 5% to 10% of those diagnosed eventually reaching ESRD, making it a major public health challenge.

Researchers have harnessed the power of artificial intelligence (AI) to tackle one of the most complex challenges in immunology: predicting how T cells recognize and respond to specific peptide antigens. Using AlphaFold 3 (AF3), a AI/ML model, designed for protein structure prediction, the team demonstrated a novel approach to model T cell receptor–peptide/major histocompatibility complex (TCR-pMHC) interactions with growing accuracy.

A team of researchers at the Icahn School of Medicine at Mount Sinai has developed a new method to identify and reduce biases in datasets used to train machine-learning algorithms - addressing a critical issue that can affect diagnostic accuracy and treatment decisions. The findings were published in the September 4 online issue of the Journal of Medical Internet Research.

How physicians feel about artificial intelligence (AI) in medicine has been studied many times. But what do patients think? A team led by researchers at the Technical University of Munich (TUM) has investigated this for the first time in a large study spanning six continents. The central finding: the worse people rate their own health, the more likely they are to reject the use of AI.

A new study in the academic journal Machine Learning: Health discovers that ChatGPT can accelerate patient screening for clinical trials, showing promise in reducing delays and improving trial success rates.

Researchers at UT Southwestern Medical Centre used ChatGPT to assess whether patients were eligible to take part in clinical trials and were able to identify suitable candidates within minutes.

An international research team led by Assistant Professor Zhiyu Wan from ShanghaiTech University has recently published groundbreaking findings in the journal Health Data Science, highlighting biases in multimodal large language models (LLMs) such as ChatGPT-4 and LLaVA in diagnosing skin diseases from medical images.

In a new study, artificial intelligence (AI) matched and potentially exceeded the performance of gastroenterologists and conventional scoring in evaluating endoscopies of Crohn’s disease patients.

The results, published in Clinical Gastroenterology and Hepatology, show a computer vision model identified mucosal ulceration as accurately as physician reviewing videos, while also being strongly correlated with the most common endoscopic scoring system.